I use Excel’s COUNTIF function a LOT for reporting. When I want to count the number of transactions that occurred per day (or during a date range), it’s easy enough to get the list of IF’s to count. But when I need to find the occurrence of different text strings, I need a unique list of the strings first. “Remove duplicates” quickly exactly what I need.

In this example, I have a list of all employees and contractor’s departments and titles – I want to know how many people are in each department and how many people have each title. Removing duplicates modifies the data, so the first step is to make a copy of the spreadsheet. Highlight the data. Select “Data” on the ribbon bar, then select “Remove Duplicates”

Select the column(s) where you want to remove duplicate data. This could be exact duplicates across multiple columns (e.g. the unique “City, State” combinations), or (in this case) I just want a unique list of departments. Click OK.

A summary will be displayed showing you how many records were removed and how many unique values remain.

Now that I have a complete listing of departments, I can use my COUNTIF function to show how many employees and contractors are in each department.

Remove duplicates only deletes records within the highlighted data. Here, I have a list of all employee titles next to the department and count info we just created. If I highlight just the ‘Title’ data and click “Remove Duplicates”, the department and count information is left unchanged.

Now I have a unique list of titles as well.

Reading through large tables of data is inefficient – it’s time consuming, error prone, and just not a heap of fun. Graphs are one way to visualize data – allowing you to quickly spot trends, outliers, etc. Excel offers another way to visually enhance data to make it more comprehensible – conditional formatting. Where some charts and graphs obscure the underlying data, conditional formatting allows the exact value to be quickly identified.

Highlight your data. On the ribbon bar, select “Home” and click the drop-down for “Conditional Formatting”.

Select the logic to determine which cells are highlighted – we’ll go through a few examples here, but click around on your own! To highlight cells that are higher than some value, select “Highlight Cell Rules” and then select “Greater Than”.

In the window that appears, enter the number and select the colouring scheme. The prepopulated number will be the average of the highlighted data. The changes are applied as you select formatting options, so you have an idea what it’ll look like ahead of time. In this case, there are still a lot of values higher than 125. I could increase my number to reduce the number of highlighted cells. When you have finished composing your formatting rule, click OK.

And the format is applied to your data. You can apply multiple formats – add another format to turn anything below 25 green, make values between 100 and 124 yellow. Whatever you want.

If you need to change your formatting rules, click on the “Conditional Formatting” drop-down and select “Manage Rules”.

If your rules do not appear, change “Current Selection” at the top to “This Worksheet”.

You can also define custom rules. From the “Conditional Formatting” drop down, select “New Rule”.

Again, select the logic used to determine which cells are formatted. Here, I am highlighting duplicated values. Click “Format” to define how the highlighted cells should appear. Click “OK” to apply the formatting to your spreadsheet.

Now every duplicated record is in green with a strike through the value.

Formatting rules can be nuanced – here I am creating a custom formatting rule that uses a three-colour gradient based on where a value falls within a range.

Now you can quickly compare each value by it’s colour.

I remember visiting my uncle at a NASA design lab sometime in the mid-80’s – it was a huge cavernous room that he explained used to house the computer. A computer his graphing calculator could draw circles around. It was a powerful visual reminder how quickly computing technology advances – components are smaller, more powerful, and simpler to use.

More than two decades ago, I wrote a visualization application that presented a graphical representation of the geographic distribution of records. Which is a long way of saying it showed where something happened to a lot of people. The application was part of a cooperative effort between the FBI and local law enforcement – a data mining project meant to identify serial offenders across jurisdictional boundaries I wanted to be able to visualize where different types of crime were occurring and identify anomalies, so I built a program to do so. It took months to develop and took hours to crunch values and draw a map. The first time I used Excel to visualize frequency distribution on a map, I thought of that NASA computer room. What used to take a high-end Unix server with a RISC processor and tonnes (for the time) of memory – not to mention an entire summer of code development – is clickity-click and done on my little laptop. And the results are nicer:

How do you create this type of visualization? First you need data with something that is mappable – the example here is going to show the office locations listed in PeopleSoft. Click within the data set.

On the ribbon bar, select “Insert” then select “3D Map” in the “Tours” section.

If you have not used it before, you will be asked to enable data analysis service.

A new window will be displayed – select the column you want to map. Here, I am using zip codes, which is mapped to the “Postal Code” field in my spreadsheet. If your fields do not map automatically, you will need to click the drop-down next to a location data type and select the appropriate column.

There are different types of visualization – here, I have switched to a “heat map” where the color of the blob represents how many records fall into this zip code. It is a quick way of identifying clusters – hot spots.

You can control the look of the map as well – here, I have switched to a flat map and added location labels.

If you would like to include a copy of your map in another program – say, this Word document – select “Capture Screen” from the ribbon bar. You can also create a video to show an animated view of your map (zooming in on specific locations, rotating the globe to see people over in Mongolia)

After you’ve clicked “Screen Capture”, just paste and an image of your map will be inserted into your file – see!

Going A Little Farther:

Data isn’t perfect, and even when the data looks good it may not map properly. My sister used to live on a street in New Jersey that does not exist on a map. The post office affirmed it was the correct address, but UPS and FedEx claimed it didn’t exist. It was funny to me, but I wasn’t the one trekking two kids down to the neighbor on the main road who nicely accepted packages for her. She moved before they ever got the address situation sorted, but I’ve got first-hand experience with addresses that don’t map in some systems but are perfectly fine in others. Why do I mention this? The map visualization provides a “Mapping confidence” statistic – it is the percentage that appears above the box where you select the location data to be mapped. 98% is pretty good – there are a handful of records that don’t appear on the map … but the data I am presenting is a decent representation of our employee office locations. A low percentage would indicate that your map does not accurately convey your data.

What if my map confidence level is low? Click on the map confidence value to see what didn’t map. There are some marked with a result that is questionable – spot-checking them, 03109 is Manchester NH and 10001 is New York, NY. The one with no resolution, according to the US Postal Service lookup isn’t a valid postal code. If your data is wrong, fix it 😊 In cases where the data is right but the application isn’t confident about the location, you can add additional data to make the address more specific (here, I might increase the confidence by having the zip+4, or including the street address in my data set).

You can filter data in your map – first we’ll need some field on which to filter. Here, I’ve added the employee’s department to my data set.

On the right-hand pane, expand “Filters”. Click “Add filter”.

Select the column on which to filter data. A unique list of values will be presented – you can scroll through it or start typing the value to search. Once you find what you want to display, click the check-box before the value.

Now we are visualizing where people in my department work.

If your data is hard to see – records are distributed out fairly evenly across the map – you can increase the area of influence to make smaller clusters easier to identify. Scroll to the bottom of the right-hand pane and drag the “Radius of influence” slider to the right. If you have very clustered data, you can drag the slider to the left to turn a large red blob into a more nuanced visualization.

When you have finished visualizing your data, click “File” on the ribbon bar and select “Close”.